{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Examples\n",
    "## Making a Monte Carlo parameter study\n",
    "In this example, we will make a Monte Carlo study. This is a case where the python library shows it's advantage. \n",
    "\n",
    "We will make a Monte Carlo study on the position of patella tendon insertion and origin in the simplified knee model used in the first tutorial. Thus, we need some macros that change two `sRel` variables in the model. In this case, we choose the values from a truncated normal distribution, but any statistical distribution could have been used. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import distributions\n",
    "# Truncated normal between +/- 2SD.\n",
    "patella_tendon_insertion = distributions.truncnorm(-2,2,[0.02, 0.12, 0], [0.01,0.01,0.01]) \n",
    "patella_tendon_origin = distributions.truncnorm(-2,2,[0.0,-0.03, 0], [0.01,0.01,0.01]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[classoperation Main.MyModel.Tibia.Patella2.sRel \"Set Value\" --value=\"{0.02,0.12,0}\",\n",
       " classoperation Main.MyModel.Patella.Lig.sRel \"Set Value\" --value=\"{0,-0.03,0}\"]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from anypytools import macro_commands as mc\n",
    "\n",
    "macro = [\n",
    "    mc.SetValue_random('Main.MyModel.Tibia.Patella2.sRel', patella_tendon_insertion),\n",
    "    mc.SetValue_random('Main.MyModel.Patella.Lig.sRel', patella_tendon_origin)\n",
    "]\n",
    "\n",
    "macro "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default representation of the macro is 50% percentile values from the distribution (i.e. the mean value). To generate something random we need the `AnyMacro` helper class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from anypytools import AnyMacro\n",
    "\n",
    "mg = AnyMacro(macro)\n",
    "monte_carlo_macros = mg.create_macros_MonteCarlo(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first generated macro just has the default values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['classoperation Main.MyModel.Tibia.Patella2.sRel \"Set Value\" --value=\"{0.02,0.12,0}\"',\n",
       " 'classoperation Main.MyModel.Patella.Lig.sRel \"Set Value\" --value=\"{0,-0.03,0}\"']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte_carlo_macros[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next two macros have random offsets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['classoperation Main.MyModel.Tibia.Patella2.sRel \"Set Value\" --value=\"{0.0349370118042,0.117548588273,0.000665700208441}\"',\n",
       "  'classoperation Main.MyModel.Patella.Lig.sRel \"Set Value\" --value=\"{0.00420981441473,-0.0166200345869,-0.000899680353891}\"'],\n",
       " ['classoperation Main.MyModel.Tibia.Patella2.sRel \"Set Value\" --value=\"{0.00300991216006,0.110408952879,-0.00162074219545}\"',\n",
       "  'classoperation Main.MyModel.Patella.Lig.sRel \"Set Value\" --value=\"{0.0116820435986,-0.044804438113,0.0063170238737}\"'],\n",
       " ['classoperation Main.MyModel.Tibia.Patella2.sRel \"Set Value\" --value=\"{0.022266645204,0.11401744759,0.0078265828213}\"',\n",
       "  'classoperation Main.MyModel.Patella.Lig.sRel \"Set Value\" --value=\"{-0.00816721013142,-0.0346727506743,-0.0153437146327}\"']]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte_carlo_macros[1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us expand the macro to also load and run the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "macro = [\n",
    "    mc.Load('Knee.any'),\n",
    "    mc.SetValue_random('Main.MyModel.Tibia.Patella2.sRel', patella_tendon_insertion ) ,\n",
    "    mc.SetValue_random('Main.MyModel.Patella.Lig.sRel', patella_tendon_origin ) ,\n",
    "    mc.OperationRun('Main.MyStudy.InverseDynamics'),\n",
    "    mc.Dump('Main.MyStudy.Output.Abscissa.t'),\n",
    "    mc.Dump('Main.MyStudy.Output.MaxMuscleActivity')\n",
    "]\n",
    "mg = AnyMacro(macro, seed=1)\n",
    "monte_carlo_macros = mg.create_macros_MonteCarlo(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the Monte Carlo macro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The macro is passed to the AnyPyProcess object which excutes the macros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "012eb0e14fab4134b8d3277c476d6969",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Completed: 100\n"
     ]
    }
   ],
   "source": [
    "from anypytools import AnyPyProcess \n",
    "\n",
    "app = AnyPyProcess()\n",
    "monte_carlo_results = app.start_macro( monte_carlo_macros )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output object (`monte_carlo_result`) is a list-like object where each element is a dictionary with the output of the corresponding simulation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length : 100\n",
      "Data keys in first element:  ['Main.MyStudy.Output.Abscissa.t', 'Main.MyStudy.Output.MaxMuscleActivity', 'task_macro_hash', 'task_id', 'task_work_dir', 'task_name', 'task_processtime', 'task_macro', 'task_logfile']\n"
     ]
    }
   ],
   "source": [
    "print('Length :', len(monte_carlo_results) )\n",
    "print('Data keys in first element: ', list(monte_carlo_results[0].keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering results with Errors\n",
    "If any model errors occured in some of the simulations, then the output may be missing. That can be a problem for futher processing. So we may want to remove those simulations. \n",
    "\n",
    "That is easily done. Results from simuations with errors will contain a  an 'ERROR' key. We can use that to filter the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "monte_carlo_results[:] = [\n",
    "    output \n",
    "    for output in monte_carlo_results \n",
    "    if 'ERROR' not in output\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting data from the resutls\n",
    "The object looks and behaves like a list, but it can do more things than a standard Python list. If we use it as a dictionary and pass the variable names, it will return that variable concatenated over all runs. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00890538, 0.00927552, 0.00986515, ..., 0.00986515, 0.00927552,\n",
       "        0.00890538],\n",
       "       [0.00761831, 0.00793615, 0.00844398, ..., 0.00844398, 0.00793614,\n",
       "        0.00761831],\n",
       "       [0.00965061, 0.01004897, 0.0106812 , ..., 0.0106812 , 0.01004897,\n",
       "        0.00965062],\n",
       "       ...,\n",
       "       [0.01007754, 0.0104962 , 0.01116247, ..., 0.01116247, 0.01049619,\n",
       "        0.01007754],\n",
       "       [0.01026166, 0.01068757, 0.01136452, ..., 0.01136452, 0.01068757,\n",
       "        0.01026167],\n",
       "       [0.00981378, 0.01021894, 0.01086207, ..., 0.01086207, 0.01021893,\n",
       "        0.00981379]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte_carlo_results['Main.MyStudy.Output.MaxMuscleActivity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, we don't even have to use the full name of the variable. As long as the string uniquely defines the variable in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_muscle_activity = monte_carlo_results['MaxMus']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the results\n",
    "Finally we can plot the result of the Monte Carlo study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23b11e2d648>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "time = monte_carlo_results['Abscissa.t'][0]\n",
    "plt.fill_between(time, max_muscle_activity.min(0), max_muscle_activity.max(0),alpha=0.4  )\n",
    "for trace in max_muscle_activity:\n",
    "    plt.plot(time, trace,'b', lw=0.2 )\n",
    "# Plot result with the mean of the inputs ( stored in the first run)\n",
    "plt.plot(time, max_muscle_activity[0],'r--', lw = 2, ) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latin Hypercube sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Monte Carlo studies are not very efficient when investigating the effect of many parameters. It quickly becomes necessary to run the model thousands of times. Not very convenient if the AnyBody model takes a long time to run. \n",
    "\n",
    "Another approach is to use Latin Hypercube sampling. From Wikipedia\n",
    "> Latin hypercube sampling (LHS) is a statistical method for generating a sample of plausible collections of parameter values from a multidimensional distribution. The sampling method is often used to construct computer experiments.\n",
    "\n",
    "Using LHS we can generate a sample that better spans the whole multidimensional space. Thus, fever model evaluations are necessary. See [pyDOE](http://pythonhosted.org/pyDOE/randomized.html) for examples (and explanation of the `criterion`/`iterations` parameters which can be parsed to `create_macros_LHS()`).\n",
    "\n",
    "To following uses LHS to do the same as in the previous example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "patella_tendon_insertion = distributions.norm([0.02, 0.12, 0], [0.01,0.01,0.01]) \n",
    "patella_tendon_origin = distributions.norm([0.0,-0.03, 0], [0.01,0.01,0.01]) \n",
    "\n",
    "macro = [ \n",
    "    mc.Load('Knee.any'),\n",
    "    mc.SetValue_random('Main.MyModel.Tibia.Patella2.sRel', patella_tendon_insertion ) ,\n",
    "    mc.SetValue_random('Main.MyModel.Patella.Lig.sRel', patella_tendon_origin ) ,\n",
    "    mc.OperationRun('Main.MyStudy.InverseDynamics'),\n",
    "    mc.Dump('Main.MyStudy.Output.Abscissa.t'),\n",
    "    mc.Dump('Main.MyStudy.Output.MaxMuscleActivity')\n",
    "]\n",
    "mg = AnyMacro(macro, seed=1)\n",
    "LHS_macros = mg.create_macros_LHS(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e874cae8c2f945d48a609a7a4ce84be6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=25.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Completed: 25\n"
     ]
    }
   ],
   "source": [
    "from anypytools import AnyPyProcess \n",
    "\n",
    "app = AnyPyProcess()\n",
    "lhs_results = app.start_macro(LHS_macros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23b12067948>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "time = lhs_results['Abscissa.t'][0]\n",
    "plt.fill_between(time, lhs_results['MaxMus'].min(0), lhs_results['MaxMus'].max(0),alpha=0.4  )\n",
    "for trace in lhs_results['MaxMus']:\n",
    "    plt.plot(time, trace,'b', lw=0.2 )\n",
    "# Plot the mean value that was stored in the first results\n",
    "plt.plot(time, lhs_results['MaxMus'].mean(0),'r--', lw = 2, ) \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
